FRTree Planner: Robot Navigation in Cluttered and Unknown Environments with Tree of Free Regions

2024-10-26

arXiv.org

AI Summary

Robot navigation in unknown, densely cluttered environments—particularly those containing narrow passages—remains challenging due to limited observability, geometric constraints, and dynamic obstacles. Method: We propose an incremental navigation framework based on the Free Region Tree (FRTree). It dynamically constructs an FRTree to encode the geometry and topology of collision-free space; introduces an online reachability analysis mechanism incorporating robot-specific geometric constraints for narrow-passage feasibility assessment and intelligent intermediate goal selection; and employs a two-layer trajectory optimization scheme—topological planning at the upper layer and platform-specific geometric refinement at the lower layer—integrated with real-time sensor fusion and mapless replanning. Results: Extensive experiments in simulation and real-world settings demonstrate significant improvements over baselines in robustness, reduced detouring, and high success rates in narrow-passage traversal, while effectively resolving dead-end avoidance and dynamic obstacle negotiation.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Search and Optimization: Learning to Search

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

In this work, we present FRTree planner, a novel robot navigation framework that leverages a tree structure of free regions, specifically designed for navigation in cluttered and unknown environments with narrow passages. The framework continuously incorporates real-time perceptive information to identify distinct navigation options and dynamically expands the tree toward explorable and traversable directions. This dynamically constructed tree incrementally encodes the geometric and topological information of the collision-free space, enabling efficient selection of the intermediate goals, navigating around dead-end situations, and avoidance of dynamic obstacles without a prior map. Crucially, our method performs a comprehensive analysis of the geometric relationship between free regions and the robot during online replanning. In particular, the planner assesses the accessibility of candidate passages based on the robot's geometries, facilitating the effective selection of the most viable intermediate goals through accessible narrow passages while minimizing unnecessary detours. By combining the free region information with a bi-level trajectory optimization tailored for robots with specific geometries, our approach generates robust and adaptable obstacle avoidance strategies in confined spaces. Through extensive simulations and real-world experiments, FRTree demonstrates its superiority over benchmark methods in generating safe, efficient motion plans through highly cluttered and unknown terrains with narrow gaps.

Problem

• Navigation in cluttered environments
• Dynamic obstacle avoidance
• Efficient path planning in narrow passages

Innovation

• Tree of free regions
• Dynamic obstacle avoidance
• Bi-level trajectory optimization